TY - JOUR AU -沈翠华AU -陈，Anfan AU -罗，陈AU -张，Jingwen AU - Feng, Bo AU -廖，Wang PY - 2020 DA - 2020/5/28 TI -利用社交媒体上的症状和诊断报告预测中国大陆COVID-19病例数:观察性信息监测研究JO - J Med Internet Res SP - e19421 VL - 22 IS - 5 KW - COVID-19 KW - SARS-CoV-2 KW -新型冠状病毒KW -传染病KW -社交媒体KW -微博KW -中国KW -疾病监测KW -监测KW -信息监测KW -信息流行病学AB -背景:冠状病毒疾病(COVID-19)已影响到全球200多个国家和地区。这种疾病给公共卫生系统带来了非同寻常的挑战，因为筛查和监测能力往往严重有限，特别是在疫情暴发初期;这可能加剧疫情的爆发，因为许多患者会在不知情的情况下感染他人。目的:本研究的目的是收集和分析微博(中国流行的类似twitter的社交媒体网站)上与COVID-19相关的帖子。据我们所知，这项信息监测研究使用了迄今为止最大、最全面和最精细的社交媒体数据来预测中国大陆的COVID-19病例数。方法:我们建立了一个2.5亿人的微博用户池，大约是整个月活跃微博用户总数的一半。使用167个关键词的综合列表，我们从2019年11月1日至2020年3月31日的用户池中检索和分析了约1500万篇与covid -19相关的帖子。我们开发了一个机器学习分类器来识别“病贴”，用户在病贴中报告自己或他人与COVID-19相关的症状和诊断。然后，我们使用官方报告的病例数作为结果，根据每日病例数估计了生病岗位和其他COVID-19岗位的格兰杰因果关系。 For a subset of geotagged posts (3.10% of all retrieved posts), we also ran separate predictive models for Hubei province, the epicenter of the initial outbreak, and the rest of mainland China. Results: We found that reports of symptoms and diagnosis of COVID-19 significantly predicted daily case counts up to 14 days ahead of official statistics, whereas other COVID-19 posts did not have similar predictive power. For the subset of geotagged posts, we found that the predictive pattern held true for both Hubei province and the rest of mainland China regardless of the unequal distribution of health care resources and the outbreak timeline. Conclusions: Public social media data can be usefully harnessed to predict infection cases and inform timely responses. Researchers and disease control agencies should pay close attention to the social media infosphere regarding COVID-19. In addition to monitoring overall search and posting activities, leveraging machine learning approaches and theoretical understanding of information sharing behaviors is a promising approach to identify true disease signals and improve the effectiveness of infoveillance. SN - 1438-8871 UR - //www.mybigtv.com/2020/5/e19421/ UR - https://doi.org/10.2196/19421 UR - http://www.ncbi.nlm.nih.gov/pubmed/32452804 DO - 10.2196/19421 ID - info:doi/10.2196/19421 ER -